Spatial Network Big Databases Queries and Storage Methods
This book provides a collection of concepts, algorithms, and techniques that effectively harness the power of Spatial Network Big Data. Reading this book is a first step towards understanding the immense challenges and novel applications of SNBD database
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atial Network Big Databases Queries and Storage Methods
Spatial Network Big Databases
KwangSoo Yang Shashi Shekhar •
Spatial Network Big Databases Queries and Storage Methods
123
Shashi Shekhar Department of Computer Science and Engineering University of Minnesota Minneapolis, MN USA
KwangSoo Yang Computer Science Department Florida Atlantic University Boca Raton, FL USA
ISBN 978-3-319-56656-6 DOI 10.1007/978-3-319-56657-3
ISBN 978-3-319-56657-3
(eBook)
Library of Congress Control Number: 2017937267 © Springer International Publishing AG 2017 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
Spatial Network Big Data (SNBD) refers to spatial network datasets whose size, variety, or update rate exceeds the capacity of commonly used spatial network computing and spatial network database technologies to learn, manage, and process with reasonable effort. SNBD has the potential to transform society via nextgeneration routing services, emergency and disaster response, and discovery of potentially useful patterns embedded in these datasets. The use of SNBD is rapidly expanding into the transportation arena to improve the management and security of transportation infrastructure and enable data-driven decision-making. However, there are significant challenges to the use of SNBD because current methods, models, and algorithms do not always scale and/or perform well when storing, managing, and analyzing large volumes of data. Interestingly, most of the SNBD collected today are not used at all, and data that are used are not fully exploited. In addition, SNBD datasets tend to be used mostly for real-time control or anomaly detection, rather
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